Battery diagnosis apparatus, battery diagnosis method, battery pack, and vehicle

ABSTRACT

A battery diagnosis apparatus for diagnosis of a cell group including a plurality of battery cells connected in series, includes a voltage sensing circuit configured to periodically generate a voltage signal indicating a cell voltage of each battery cell, and a control circuit configured to generate time series data indicating a change in cell voltage of each battery cell over time based on the voltage signal. The control circuit is configured to (i) determine a first average cell voltage and a second average cell voltage of each battery cell based on the time series data, wherein the first average cell voltage is a short term moving average, and the second average cell voltage is a long term moving average, and (ii) detect an abnormal voltage of each battery cell based on a difference between the first average cell voltage and the second average cell voltage.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 17/918,774 filed Oct. 13, 2022, which application is a nationalphase entry under 35 U.S.C. § 371 of International Application No.PCT/KR2021/017684 filed Nov. 26, 2021, claims priority from KoreanPatent Application No. 10-2020-0163366 filed on Nov. 27, 2020, all ofwhich are incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to technology for abnormal voltagediagnosis of a battery.

BACKGROUND ART

Recently, there has been a rapid increase in the demand for portableelectronic products such as laptop computers, video cameras and mobilephones, and with the extensive development of electric vehicles,accumulators for energy storage, robots and satellites, many studies arebeing made on high performance batteries that can be rechargedrepeatedly.

Currently, commercially available batteries include nickel-cadmiumbatteries, nickel-hydrogen batteries, nickel-zinc batteries, lithiumbatteries and the like, and among them, lithium batteries have little orno memory effect and thus they are gaining more attention thannickel-based batteries for their advantages that recharging can be donewhenever it is convenient, the self-discharge rate is very low and theenergy density is high.

Recently, with the widespread of applications (for example, energystorage systems, electric vehicles) requiring high voltage, there is arising need for accurate diagnosis of abnormal voltage of each of aplurality of battery cells connected in series in a battery pack.

Abnormal voltage conditions of a battery cell refers to faultyconditions caused by abnormal drop and/or rise of cell voltage due to aninternal short circuit, an external short circuit, a defect in a voltagesensing line, bad connection with a charge/discharge line, and the like.

Attempts have been made to carry out abnormal voltage diagnosis of eachbattery cell by comparing a voltage across each battery cell, namely, acell voltage, at a specific time with an average cell voltage of theplurality of battery cells at the same time as the specific time.However, the cell voltage of each battery cell relies on thetemperature, current and/or State Of Health (SOH) of the correspondingbattery cell, so it is difficult to achieve accurate diagnosis ofabnormal voltage of each battery cell by simply comparing the cellvoltages of the plurality of battery cells measured at the specifictime. For example, when there is a large difference in temperature orSOH between a battery cell having no abnormal voltage and the remainingbattery cells, a difference between the cell voltage of thecorresponding battery cell and the average cell voltage may be alsolarge.

To solve this problem, in addition to the cell voltage of each batterycell, additional parameters such as the charge/discharge current, thetemperature of each battery cell and/or State Of Charge (SOC) of eachbattery cell may be used for abnormal voltage diagnosis of each batterycell. However, the diagnosis method using the additional parametersinvolves a process of detecting each parameter and a process ofcomparing the parameters, and thus requires more complexity and a longertime than diagnosis methods using the cell voltage as a singleparameter.

SUMMARY Technical Problem

The present disclosure is designed to solve the above-described problem,and therefore the present disclosure is directed to providing a batterydiagnosis apparatus, a battery diagnosis method, a battery pack and avehicle for efficient and accurate abnormal voltage diagnosis of abattery cell, in which an moving average of cell voltage of each of aplurality of battery cells is determined at each unit time for each ofat least one moving window having a given time length, and abnormalvoltage diagnosis of each battery cell is performed based on each movingaverage of each battery cell.

These and other objects and advantages of the present disclosure may beunderstood by the following description and will be apparent from theembodiments of the present disclosure. In addition, it will be readilyunderstood that the objects and advantages of the present disclosure maybe realized by the means set forth in the appended claims and acombination thereof.

Technical Solution

A battery diagnosis apparatus for achieving the above-described objectis a battery diagnosis apparatus for a cell group including a pluralityof battery cells connected in series, and may include a voltage sensingcircuit configured to periodically generate a voltage signal indicatinga cell voltage of each battery cell of the plurality of battery cells;and a control circuit configured to for each battery cell of theplurality of battery cells, generate time series data indicating achange in cell voltage of the battery cell over time based on thevoltage signal.

Preferably, the control circuit may be configured to (i) for eachbattery cell of the plurality of battery cells, determine a firstaverage cell voltage and a second average cell voltage of the batterycell based on the time series data, wherein the first average cellvoltage is a short term moving average, and the second average cellvoltage is a long term moving average, and (ii) detect an abnormalvoltage of at least one battery cell based on a difference between thefirst average cell voltage and the second average cell voltage of the atleast one battery cell.

In an aspect, the control circuit may be configured to for each batterycell of the plurality of battery cells, determine a short/long termaverage difference of the battery cell corresponding to the differencebetween the first average cell voltage and the second average cellvoltage of the battery cell, for each battery cell of the plurality ofbattery cells, determine a cell diagnosis deviation of the battery cellcorresponding to a deviation between an average value of short/long termaverage differences of all the plurality of battery cells and theshort/long term average difference of the battery cell, and in responseto the cell diagnosis deviation exceeding a diagnosis threshold for theat least one battery cell, determine that the at least one battery cellis an abnormal voltage cell.

Preferably, the control circuit may be configured to, for each batterycell of the plurality of battery cells, generate time series data of thecell diagnosis deviation of the battery cell, and determine that atleast one battery cell of the plurality of battery cells is an abnormalvoltage cell either (i) after a period of time during which the celldiagnosis deviation exceeds the diagnosis threshold or (ii) after anumber of cell diagnosis deviations exceeding the diagnosis thresholdexceeds a predetermined number.

In another aspect, the control circuit may be configured to for eachbattery cell of the plurality of battery cells, determine a short/longterm average difference of the battery cell corresponding to thedifference between the first average cell voltage and the second averagecell voltage of the battery cell, for each battery cell of the pluralityof battery cells, determine a cell diagnosis deviation of the batterycell corresponding to a deviation between an average value of short/longterm average differences of all the plurality of battery cells and theshort/long term average difference of the battery cell, determine astatistical adaptive threshold based on a standard deviation for thecell diagnosis deviation of all the plurality of battery cells, for eachbattery cell of the plurality of battery cells, generate time seriesdata of a filter diagnosis value of the battery cell by filtering timeseries data for the cell diagnosis deviation of the battery cell basedon the statistical adaptive threshold, and detect the abnormal voltageof the at least one battery cell based on a period of time during whichthe filter diagnosis value of the at least one battery cell exceeds adiagnosis threshold, or based on a number of the filter diagnosis valueexceeding the diagnosis threshold.

In still another aspect, the control circuit may be configured to foreach battery cell of the plurality of battery cells, determine ashort/long term average difference of the battery cell corresponding tothe difference between the first average cell voltage and the secondaverage cell voltage of the battery cell, for each battery cell of theplurality of battery cells, determine a normalization value of theshort/long term average difference of the battery cell as a normalizedcell diagnosis deviation, determine a statistical adaptive thresholdbased on a standard deviation for the normalized cell diagnosisdeviation of all the plurality of battery cells, for each battery cellof the plurality of battery cells, generate time series data of a filterdiagnosis value by filtering time series data for the normalized celldiagnosis deviation of the battery cell based on the statisticaladaptive threshold, and detect the abnormal voltage of at least onebattery cell based on a period of time during which the filter diagnosisvalue of the at least one battery cell exceeds a diagnosis threshold, orbased on a number of the filter diagnosis value exceeding the diagnosisthreshold.

Preferably, the control circuit may for each battery cell of theplurality of battery cells, normalize the short/long term averagedifference of the battery cell by dividing the short/long term averagedifference of the battery cell by an average value of short/long termaverage differences of all the plurality of battery cells.

Alternatively, the control circuit may for each battery cell of theplurality of battery cells, normalize the short/long term averagedifference of the battery cell through log calculation of the short/longterm average difference of the battery cell.

In another aspect, the control circuit may be configured to for eachbattery cell of the plurality of battery cells, generate time seriesdata indicating a change in cell voltage of the battery cell over timeusing a voltage difference between a cell voltage average value of allthe plurality of battery cells and a cell voltage of the battery cell,measured at each unit time.

In still another aspect, the control circuit may be configured to foreach battery cell of the plurality of battery cells, determine ashort/long term average difference of the battery cell corresponding tothe difference between the first average cell voltage and the secondaverage cell voltage of the battery cell, for each battery cell of theplurality of battery cells, determine a normalization value of theshort/long term average difference of the battery cell as a normalizedcell diagnosis deviation of the battery cell, generate time series dataof the normalized cell diagnosis deviation of the battery cell, for eachbattery cell of the plurality of battery cells, generate the time seriesdata of the normalized cell diagnosis deviation of the battery cell byrecursively repeating: (i) determining a first moving average and asecond moving average of the battery cell for the time series data ofthe normalized cell diagnosis deviation of the battery cell, wherein thefirst moving average is a short term moving average of the battery celland the second moving average is a long term moving average of thebattery cell, (ii) determining the short/long term average difference ofthe battery cell corresponding to a difference between the first movingaverage and the second moving average of the battery cell, (iii)determining the normalization value of the short/long term averagedifference of the battery cell as the normalized cell diagnosisdeviation, and (iv) generating the time series data of the normalizedcell diagnosis deviation of the battery cell, determine a statisticaladaptive threshold based on a standard deviation for the normalized celldiagnosis deviation of all the plurality of battery cells, for eachbattery cell of the plurality of battery cells, generate time seriesdata of a filter diagnosis value of the battery cell by filtering thetime series data for the normalized cell diagnosis deviation of thebattery cell based on the statistical adaptive threshold, and detect theabnormal voltage of at least one battery cell based on a period of timeduring which the filter diagnosis value of the at least one battery cellexceeds a diagnosis threshold, or based on a number of the filterdiagnosis value exceeding the diagnosis threshold.

A battery diagnosis method according to the present disclosure forachieving the above-described object is a battery diagnosis method for acell group including a plurality of battery cells connected in series,and may include (a) for each battery cell of the plurality of batterycells, periodically generating, by one or more processors, time seriesdata indicating a change in cell voltage of the battery cell over time;(b) for each battery cell of the plurality of battery cells,determining, by the one or more processors, a first average cell voltageand a second average cell voltage of the battery cell based on the timeseries data, wherein the first average cell voltage is a short termmoving average, and the second average cell voltage is a long termmoving average; and (c) detecting, by the one or more processors, anabnormal voltage of at least one battery cell based on a differencebetween the first average cell voltage and the second average cellvoltage of the at least one battery cell.

In an aspect, the step (c) may include (c1) for each battery cell of theplurality of battery cells, determining, by the one or more processors,a short/long term average difference of the battery cell correspondingto the difference between the first average cell voltage and the secondaverage cell voltage of the battery cell; (c2) determining, by the oneor more processors, a cell diagnosis deviation of the battery cellcorresponding to a deviation between an average value of short/long termaverage differences of all the plurality of battery cells and theshort/long term average difference of the battery cell of the batterycell; and (c3) in response to the cell diagnosis deviation exceeding adiagnosis threshold for the at least one battery cell, determining, bythe one or more processors, that the at least one battery cell is anabnormal voltage cell.

Preferably, the step (c) may include (c1) for each battery cell of theplurality of battery cells, generating, by the one or more processors,time series data of the cell diagnosis deviation of the battery cell,wherein determining that at least one battery cell of the plurality ofbattery cells is an abnormal voltage cell occurs either after a periodof time during which the cell diagnosis deviation exceeds the diagnosisthreshold or after a number of cell diagnosis deviations exceeding thediagnosis threshold exceeds a predetermined number.

In another aspect, the step (c) may include (c1) for each battery cellof the plurality of battery cells, determining, by the one or moreprocessors, a short/long term average difference of the battery cellcorresponding to the difference between the first average cell voltageand the second average cell voltage of the battery cell; (c2)determining, by the one or more processors, a cell diagnosis deviationof the battery cell corresponding to a deviation between an averagevalue of short/long term average differences of all the plurality ofbattery cells and the short/long term average difference of the batterycell; (c3) determining, by the one or more processors, a statisticaladaptive threshold based on a standard deviation for the cell diagnosisdeviation of all the plurality of battery cells; (c4) for each batterycell of the plurality of battery cells, generating, by the one or moreprocessors, time series data of a filter diagnosis value of the batterycell by filtering time series data for the cell diagnosis deviation ofthe battery cell based on the statistical adaptive threshold; and (c5)detecting, by the one or more processors, the abnormal voltage of the atleast one battery cell based on a period of time during which the filterdiagnosis value of the at least one battery cell exceeds a diagnosisthreshold, or based on a number of the filter diagnosis value exceedingthe diagnosis threshold.

In still another aspect, the step (c) may include (c1) for each batterycell of the plurality of battery cells, determining, by the one or moreprocessors, a short/long term average difference of the battery cellcorresponding to the difference between the first average cell voltageand the second average cell voltage of the battery cell; (c2) for eachbattery cell of the plurality of battery cells, determining, by the oneor more processors, a normalization value of the short/long term averagedifference of the battery cell as a normalized cell diagnosis deviation;(c3) determining, by the one or more processors, a statistical adaptivethreshold based on a standard deviation for the normalized celldiagnosis deviation of all the plurality of battery cells; (c4) for eachbattery cell of the plurality of battery cells, generating, by the oneor more processors, time series data of a filter diagnosis value byfiltering time series data for the normalized cell diagnosis deviationof the battery cell based on the statistical adaptive threshold; and(c5) detecting, by the one or more processors, the abnormal voltage ofthe at least one battery cell based on a period of time during which thefilter diagnosis value of the at least one battery cell exceeds adiagnosis threshold, or based on a number of data of the filterdiagnosis value exceeding the diagnosis threshold.

Preferably, the step (c2) may be a step of for each battery cell of theplurality of battery cells, normalizing, by the one or more processors,the short/long term average difference of the battery cell by dividingthe short/long term average difference of the battery cell by an averagevalue of short/long term average differences of all the plurality ofbattery cells.

Alternatively, the step (c2) may be a step of for each battery cell ofthe plurality of battery cells, normalizing, by the one or moreprocessors, the short/long term average difference of the battery cellthrough log calculation of the short/long term average difference of thebattery cell.

In another aspect, the step (a) may be a step of for each battery cellof the plurality of battery cells, generating, by the one or moreprocessors, time series data indicating a change in cell voltage of thebattery cell over time using a voltage difference between a cell voltageaverage value of all the plurality of battery cells and a cell voltageof the battery cell, measured at each unit time.

In still another aspect, the step (c) may include (c1) for each batterycell of the plurality of battery cells, determining, by the one or moreprocessors, a short/long term average difference of the battery cellcorresponding to the difference between the first average cell voltageand the second average cell voltage of the battery cell; (c2) for eachbattery cell of the plurality of battery cells, determining, by the oneor more processors, a normalization value of the short/long term averagedifference of the battery as a normalized cell diagnosis deviation ofthe battery cell; (c3) for each battery cell of the plurality of batterycells, generating, by the one or more processors, time series data ofthe normalized cell diagnosis deviation of the battery cell; (c4) foreach battery cell of the plurality of battery cells, generating, by theone or more processors, the time series data of the normalized celldiagnosis deviation for each battery cell by recursively repeating: (i)determining, by the one or more processors, a first moving average and asecond moving average of the battery cell for the time series data ofthe normalized cell diagnosis deviation of the battery cell, wherein thefirst moving average is a short term moving average of the battery celland the second moving average is a long term moving average of thebattery cell, (ii) determining the short/long term average difference ofthe battery cell corresponding to a difference between the first movingaverage and the second moving average of the battery cell, (iii)determining the normalization value of the short/long term averagedifference of the battery cell as the normalized cell diagnosisdeviation, and (iv) generating the time series data of the normalizedcell diagnosis deviation of the battery cell, (c5) determining, by theone or more processors, a statistical adaptive threshold based on astandard deviation for the normalized cell diagnosis deviation of allthe plurality battery cells; (c6) for each battery cell of the pluralityof battery cells, generating, by the one or more processors, time seriesdata of a filter diagnosis value of the battery cell by filtering thetime series data for the normalized cell diagnosis deviation of thebattery cell based on the statistical adaptive threshold; and (c7)detecting, by the one or more processors, the abnormal voltage of atleast one battery cell based on a period of time during which the filterdiagnosis value of the at least one battery cell exceeds a diagnosisthreshold, or based on a number of the filter diagnosis value exceedingthe diagnosis threshold.

The above-described technical object may be also achieved by a batterypack including the battery diagnosis apparatus as described in any ofthe embodiments herein and a vehicle including the same.

Advantageous Effects

According to an aspect of the present disclosure, it is possible toachieve efficient and accurate diagnosis of abnormal voltage of eachbattery cell by determining two moving averages of cell voltage of eachbattery cell over two different time lengths at each unit time, andcarrying out abnormal voltage diagnosis of each battery cell based on adifference between the two moving averages of each of the plurality ofbattery cells.

According to another aspect of the present disclosure, it is possible toachieve accurate diagnosis of abnormal voltage of each battery cell byapplying the advanced techniques such as normalization and/orstatistical adaptive threshold to analyze a difference in variationtrend of two moving averages of each battery cell.

According to still another aspect of the present disclosure, it ispossible to precisely detect the time zone in which the abnormal voltageof each battery cell occurred and/or the abnormal voltage detectioncount by analyzing the time series data of the filter diagnosis valuedetermined based on the statistical adaptive threshold.

The effects of the present disclosure are not limited to theabove-mentioned effects, and these and other effects not mentionedherein will be clearly understood by those skilled in the art from theappended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate a preferred embodiment of thepresent disclosure, and together with the detailed description of thepresent disclosure described below, serve to provide a furtherunderstanding of the technical aspects of the present disclosure, andthus the present disclosure should not be construed as being limited tothe drawings.

FIG. 1 is a diagram showing exemplarily an electric vehicle according toan embodiment of the present disclosure.

FIGS. 2 a to 2 h are graphs referenced in describing a process forabnormal voltage diagnosis of each battery cell from time series dataindicating a change in cell voltage of each of the plurality of batterycells shown in FIG. 1 over time.

FIG. 3 is a flowchart showing exemplarily a battery diagnosis methodaccording to a first embodiment of the present disclosure.

FIG. 4 is a flowchart showing exemplarily a battery diagnosis methodaccording to a second embodiment of the present disclosure.

FIG. 5 is a flowchart showing exemplarily a battery diagnosis methodaccording to a third embodiment of the present disclosure.

FIG. 6 is a flowchart showing exemplarily a battery diagnosis methodaccording to a fourth embodiment of the present disclosure.

FIG. 7 is a flowchart showing exemplarily a battery diagnosis methodaccording to a fifth embodiment of the present disclosure.

DETAILED DESCRIPTION

Hereinafter, the preferred embodiments of the present disclosure will bedescribed in detail with reference to the accompanying drawings. Priorto the description, it should be understood that the terms or words usedin the specification and the appended claims should not be construed asbeing limited to general and dictionary meanings, but rather interpretedbased on the meanings and concepts corresponding to the technicalaspects of the present disclosure on the basis of the principle that theinventor is allowed to define the terms appropriately for the bestexplanation.

Therefore, the embodiments described herein and the illustrations shownin the drawings are just a most preferred embodiment of the presentdisclosure, but not intended to fully describe the technical aspects ofthe present disclosure, so it should be understood that a variety ofother equivalents and modifications could have been made thereto at thetime that the application was filed.

The terms including the ordinal number such as “first”, “second” and thelike, are used to distinguish one element from another among variouselements, but not intended to limit the elements by the terms.

Unless the context clearly indicates otherwise, it will be understoodthat the term “comprises” when used in this specification, specifies thepresence of stated elements, but does not preclude the presence oraddition of one or more other elements. Additionally, the term “controlunit” as used herein refers to a processing element of at least onefunction or operation, and this may be implemented by hardware andsoftware either alone or in combination.

In addition, throughout the specification, it will be further understoodthat when an element is referred to as being “connected to” anotherelement, it can be directly connected to the other element orintervening elements may be present.

FIG. 1 is a diagram showing exemplarily an electric vehicle according toan embodiment of the present disclosure.

Referring to FIG. 1 , the electric vehicle 1 includes a battery pack 2,an inverter 3, an electric motor 4 and a vehicle controller 5.

The battery pack 2 includes a cell group CG, a switch 6 and a batterymanagement system 100.

The cell group CG may be coupled to the inverter 3 through a pair ofpower terminals provided in the battery pack 2. The cell group CGincludes a plurality of battery cells BC₁˜BC_(N) (N is a natural numberof 2 or greater) connected in series. Each battery cell BC_(i) is notlimited to a particular type, and may include any battery cell that canbe recharged such as a lithium ion battery cell. i is an index for cellidentification. i is a natural number and is between 1 and N.

The switch 6 is connected in series to the cell group CG. The switch 6is installed on a current path for the charge/discharge of the cellgroup CG. The switch 6 is controlled between an on state and an offstate in response to a switching signal from the battery managementsystem 100. The switch 6 may be a mechanical relay which is turnedon/off by the electromagnetic force of a coil, or a semiconductor switchsuch as a Metal Oxide Semiconductor Field Effect transistor (MOSFET).

The inverter 3 is provided to convert the direct current (DC) power fromthe cell group CG to alternating current (AC) power in response to acommand from the battery management system 100. The electric motor 4 maybe, for example, a three-phase AC motor. The electric motor 4 operatesusing the AC power from the inverter 3.

The battery management system 100 is provided to take general controlrelated to the charge/discharge of the cell group CG.

The battery management system 100 includes a battery diagnosis apparatus200. The battery management system 100 may further include at least oneof a current sensor 310, a temperature sensor 320 and an interface unit330.

The battery diagnosis apparatus 200 is provided for abnormal voltagediagnosis of each of the plurality of battery cells BC₁˜BC_(N). Thebattery diagnosis apparatus 200 includes a voltage sensing circuit 210and a control circuit 220.

The voltage sensing circuit 210 is connected to positive and negativeelectrodes of each of the plurality of battery cells BC₁˜BC_(N) througha plurality of voltage sensing lines. The voltage sensing circuit 210 isconfigured to measure a cell voltage across each battery cell BC andgenerate a voltage signal indicating the measured cell voltage.

The current sensor 310 is connected in series to the cell group CGthrough the current path. The current sensor 310 is configured to detecta battery current flowing through the cell group CG, and generate acurrent signal indicating the detected battery current.

The temperature sensor 320 is configured to detect a temperature of thecell group CG and generate a temperature signal indicating the detectedtemperature.

The control circuit 220 may be implemented in hardware using at leastone of application specific integrated circuits (ASICs), digital signalprocessors (DSPs), digital signal processing devices (DSPDs),programmable logic devices (PLDs), field programmable gate arrays(FPGAs), microprocessors and electrical units for performing otherfunctions.

The control circuit 220 may have a memory unit. The memory unit mayinclude at least one type of storage medium of flash memory type, harddisk type, Solid State Disk (SSD) type, Silicon Disk Drive (SDD) type,multimedia card micro type, random access memory (RAM), static randomaccess memory (SRAM), read-only memory (ROM), electrically erasableprogrammable read-only memory (EEPROM), or programmable read-only memory(PROM). The memory unit may store data and programs required forcomputation by the control circuit 220. The memory unit may store dataindicating the results of computation performed by the control circuit220. In particular, the control circuit 220 may record at least one of aplurality of parameters calculated at each unit time as described belowin the memory unit.

The control circuit 220 may be operably coupled to the voltage sensingcircuit 210, the temperature sensor 320, the current sensor 310, theinterface unit 330 and/or the switch 6. The control circuit 220 maycollect a sensing signal from the voltage sensing circuit 210, thecurrent sensor 310 and the temperature sensor 320. The sensing signalrefers to the voltage signal, the current signal and/or the temperaturesignal detected in synchronization manner.

The interface unit 330 may include a communication circuit configured tosupport wired or wireless communication between the control circuit 220and the vehicle controller 5 (for example, an Electronic Control Unit(ECU). The wired communication may be, for example, controller areanetwork (CAN) communication, and the wireless communication may be, forexample, Zigbee or Bluetooth communication. The communication protocolis not limited to a particular type, and may include any communicationprotocol that supports the wired/wireless communication between thecontrol circuit 220 and the vehicle controller 5.

The interface unit 330 may be coupled to an output device (for example,a display, a speaker) which provides information received from thevehicle controller 5 and/or the control circuit 220 in a recognizableformat. The vehicle controller 5 may control the inverter 3 based onbattery information (for example, voltage, current, temperature, SOC)collected via communication with the battery management system 100.

FIGS. 2 a to 2 h are graphs showing exemplarily a process for abnormalvoltage diagnosis of each battery cell from time series data indicatinga change in cell voltage of each of the plurality of battery cells shownin FIG. 1 over time.

FIG. 2 a shows a voltage curve of each of the plurality of battery cellsBC₁˜BC_(N). The number of battery cells is 14. The control circuit 220collects the voltage signal from the voltage sensing circuit 210 at eachunit time, and records a voltage value of cell voltage of each batterycell BC_(i) in the memory unit. The unit time may be an integer multipleof the voltage measurement cycle of the voltage sensing circuit 210.

The control circuit 220 may generate cell voltage time series dataindicating a cell voltage history of each battery cell over time basedon the voltage value of cell voltage of each battery cell BC_(i)recorded in the memory unit. The number of cell voltage time series dataincreases by 1 each time the cell voltage is measured.

The plurality of voltage curves shown in FIG. 2 a correlates with theplurality of battery cells BC₁˜BC_(N) in a one-to-one relationship.Accordingly, each voltage curve indicates a cell voltage change historyof any one battery cell BC associated therewith.

The control circuit 220 may determine a moving average of each of theplurality of battery cells BC₁˜BC_(N) at each unit time using one or twomoving windows. When two moving windows are used, the time length of anyone moving window is different from the time length of the other movingwidow.

Here, the time length of each moving window is the integer multiple ofthe unit time, and the ending point of each moving window is the presenttime and the starting point of each moving window is a time point whichis a given time length earlier than the present time.

Hereinafter, for convenience of description, of the two moving windows,a moving window associated with a shorter time length is referred to asa first moving window, and a moving window associated with a longer timelength is referred to as a second moving window.

The control circuit 220 may carry out abnormal voltage diagnosis of eachbattery cell BC_(i) using the first moving window alone or both thefirst moving window and the second moving window.

The control circuit 220 may compare a short term change trend and a longterm change trend of cell voltage of an i^(th) battery cell BC_(i) ateach unit time based on the cell voltage of the i^(th) battery cellBC_(i) collected at each unit time.

The control circuit 220 may determine a first average cell voltage,i.e., a moving average of the i^(th) battery cell BC_(i) by the firstmoving window at each unit time using the following Equation 1 or 2.

Equation 1 is a moving average calculation equation by an arithmeticaverage method, and Equation 2 is a moving average calculation equationby a weighted average method.

$\begin{matrix}{{{SMA}_{i}\lbrack k\rbrack} = \frac{\sum\limits_{j = 1}^{S}{V_{i}\left\lbrack {k - S + j} \right\rbrack}}{S}} & {< {Equation}1 >}\end{matrix}$ $\begin{matrix}{{{SMA}_{i}\lbrack k\rbrack} = {\frac{{{SMA}_{i}\left\lbrack {k - 1} \right\rbrack} \times \left( {S - 1} \right)}{S} + \frac{V_{i}\lbrack k\rbrack}{S}}} & {< {Equation}2 >}\end{matrix}$

In Equations 1 and 2, k is a time index indicating the present time,SMA_(i)[k] is the first average cell voltage of the i^(th) battery cellBC_(i) at the present time, S is a value obtained by dividing the timelength of the first moving window by the unit time, and V_(i)[k] is thecell voltage of the i^(th) battery cell BC_(i) at the present time. Forexample, when the unit time is 1 sec and the time length of the firstmoving window is 10 sec, S is 10. When x is a natural number of k orsmaller, V_(i)[k−x] and SMA_(i)[k−x] indicate the cell voltage of thei^(th) battery cell BC_(i) and the first average cell voltage when thetime index is k−x, respectively. For reference, the control circuit 220may be set to increase the time index by 1 at each unit time.

The control circuit 220 may determine a second average cell voltagewhich is a moving average of the i^(th) battery cell BC_(i) by thesecond moving window at each unit time using the following Equation 3 or4.

Equation 3 is a moving average calculation equation by an arithmeticaverage method, and Equation 4 is a moving average calculation equationby a weighted average method.

$\begin{matrix}{{{LMA}_{i}\lbrack k\rbrack} = \frac{\sum\limits_{j = 1}^{L}{V_{i}\left\lbrack {k - L + j} \right\rbrack}}{L}} & {< {Equation}3 >}\end{matrix}$ $\begin{matrix}{{{LMA}_{i}\lbrack k\rbrack} = {\frac{{{LMA}_{i}\left\lbrack {k - 1} \right\rbrack} \times \left( {L - 1} \right)}{L} + \frac{V_{i}\lbrack k\rbrack}{L}}} & {< {Equation}4 >}\end{matrix}$

In Equations 3 and 4, k is the time index indicating the present time,LMA_(i)[k] is the second average cell voltage of the i^(th) battery cellBC_(i) at the present time, L is a value obtained by dividing the timelength of the second moving window by the unit time, and V_(i)[k] is thecell voltage of the i^(th) battery cell BC_(i) at the present time. Forexample, when the unit time is 1 sec and the time length of the secondmoving window is 100 sec, L is 100. When x is a natural number of k orsmaller, LMA_(i)[k−x] denotes the second average cell voltage when thetime index is k-x.

In an embodiment, the control circuit 220 may input, as V_(i)[k] ofEquation 1 to 4, a difference between a reference cell voltage of thecell group CG and the cell voltage of the battery cell BC_(i) at thepresent time, instead of the cell voltage of each battery cell BC_(i) atthe present time.

The reference cell voltage of the cell group CG at the present time isan average value of the plurality of cell voltages at the present timedetermined from the plurality of battery cells BC_(i)˜BC_(N). In avariation, the average value of the plurality of cell voltages may bereplaced with a median value thereof.

Specifically, the control circuit 220 may set VD_(i)[k] of the followingEquation 5 as V_(i)[k] of Equations 1 to 4.VD _(i) [k]=V _(av) [k]−V _(i) [k]  <Equation 5>

In Equation 5, V_(av)[k] is the reference cell voltage of the cell groupCG at the present time and is the average value of the plurality of cellvoltages.

When the time length of the first moving window is shorter than the timelength of the second moving window, the first average cell voltage maybe referred to as a ‘short term moving average’ of cell voltage, and thesecond average cell voltage may be referred to as a ‘long term movingaverage’ of cell voltage.

FIG. 2 b shows a short term moving average line and a long term movingaverage line for the cell voltage of the i^(th) battery cell BC_(i)determined from the plurality of voltage curves shown in FIG. 2 a . InFIG. 2 b , the horizontal axis indicates time, and the vertical axisindicates a short term moving average and a long term moving average ofcell voltage.

Referring to FIG. 2 b , the plurality of moving average lines S_(i)indicated in the dashed line is associated with the plurality of batterycells BC_(i)˜BC_(N) in a one-to-one relationship, and indicates a changehistory of the first average cell voltage SMA_(i)[k] of each batterycell BC over time. Additionally, the plurality of moving average linesL_(i) indicated in the solid line is associated with the plurality ofbattery cells BC₁˜BC_(N) in a one-to-one relationship, and indicates achange history of the second average cell voltage LMA_(i)[k] of eachbattery cell BC over time.

The dotted line graph and the solid line graph are obtained usingEquations 2 and 4, respectively. Additionally, VD_(i)[k] of Equation 5is used as V_(i)[k] of Equations 2 and 4, and V_(av)[k] is set as anaverage of the plurality of cell voltages. The time length of the firstmoving window is 10 sec, and the time length of the second moving windowis 100 sec.

FIG. 2 c shows a time-dependent change in short/long term averagedifference (an absolute value) corresponding to a difference between thefirst average cell voltage SMA_(i)[k] and the second average cellvoltage LMA_(i)[k] of each battery cell shown in FIG. 2 b . In FIG. 2 c, the horizontal axis indicates time, and the vertical axis indicatesthe short/long term average difference of each battery cell BC_(i).

The short/long term average difference of each battery cell BC_(i) is adifference between the first average cell voltage SMA_(i) and the secondaverage cell voltage LMA_(i) of each battery cell BC_(i) at each unittime. For example, the short/long term average difference of the i^(th)battery cell BC_(i) may be equal to a value of subtracting one (forexample, a smaller one) of SMA_(i)[k] and LMA_(i)[k] from the other (forexample, a larger one).

The short/long term average difference of the i^(th) battery cell BC_(i)relies on the short-term change history and the long-term change historyof cell voltage of the i^(th) battery cell BC_(i).

The temperature or SOH of the i^(th) battery cell BC_(i) steadilyaffects the cell voltage of the i^(th) battery cell BC_(i) for a shortterm as well as a long term. Accordingly, in case that there is noabnormal voltage in the i^(th) battery cell BC_(i) there is nosignificant difference between the short/long term average difference ofthe i^(th) battery cell BC_(i) and the short/long term averagedifferences of the remaining battery cells.

In contrast, the abnormal voltage suddenly occurred in the i^(th)battery cell BC_(i) due to an internal circuit short and/or an externalshort circuit affects the first average cell voltage SMA_(i)[k] moregreatly than the second average cell voltage LMA_(i)[k]. As a result,the short/long term average difference of the i^(th) battery cell BC_(i)has a large deviation from the short/long term average differences ofthe remaining battery cells having no abnormal voltage.

The control circuit 220 may determine the short/long term averagedifference |SMA_(i)[k]˜LMA_(i)[k]| of each battery cell BC_(i) at eachunit time. Additionally, the control circuit 220 may determine anaverage value of short/long term average differences|SMA_(i)[k]˜LMA_(i)[k]|. Hereinafter, the average value is indicated as|SMA_(i)[k]˜LMA_(i)[k]|_(av). Additionally, the control circuit 220 maydetermine, as a cell diagnosis deviation D_(diag,i)[k], a deviation forthe short/long term average difference |SMA_(i)[k]˜LMA_(i)[k]| relativeto the average value |SMA_(i)[k]˜LMA_(i)[k]|_(av) of short/long termaverage differences. Additionally, the control circuit 220 may carry outabnormal voltage diagnosis of each battery cell BC_(i) based on the celldiagnosis deviation D_(diag,i)[k].

In an embodiment, the control circuit 220 may diagnose that there is anabnormal voltage in the corresponding i^(th) battery cell BC_(i) whenthe cell diagnosis deviation D_(diag,i)[k] for the i^(th) battery cellBC_(i) exceeds a preset diagnosis threshold (for example, 0.015).

Preferably, the control circuit 220 may normalize the short/long termaverage difference |SMA_(i)[k]˜LMA_(i)[k]| of each battery cell BC_(i)using a normalization reference value for abnormal voltage diagnosis.Preferably, the normalization reference value is an average value|SMA_(i)[k]˜LMA_(i)[k]|_(av) of the short/long term average differences.

Specifically, the control circuit 220 may set the average value|SMA_(i)[k]−LMA_(i)[k]|_(av) of short/long term average differences offirst to N^(th) battery cells BC_(i)-BC_(N) as the normalizationreference value. Additionally, the control circuit 220 normalize theshort/long term average difference |SMA_(i)[k]−LMA_(i)[k]| by dividingthe short/long term average difference |SMA_(i)[k]−LMA_(i)[k]| of eachbattery cell BC_(i) by the normalization reference value.

The following Equation 6 is an equation for normalization of theshort/long term average difference |SMA_(i)[k]−LMA_(i)[k]| of eachbattery cell BC_(i). In an embodiment, the product of Equation 6 may bereferred to as a normalized cell diagnosis deviation D*_(diag,i)[k].D* _(diag,i) [k]=(|SMA _(i) [k]−LMA _(i) [k]|)÷(|SMA _(i) [k]−LMA _(i)[k]| _(av))  <Equation 6>

In Equation 6, |SMA_(i)[k]−LMA_(i)[k]| is the short/long term averagedifference of the i^(th) battery cell BC_(i) at the present time,|SMA_(i)[k]−LMA_(i)[k]|_(av) is the average value (the normalizationreference value) of short/long term average differences of all thebattery cells, and D*_(diag,i)[k] is the normalized cell diagnosedeviation of the i^(th) battery cell BC_(i) at the present time. Thesymbol indicates that the parameter is normalized.

The short/long term average difference |SMA_(i)−LMA_(i)[k]| of eachbattery cell BC_(i) may be normalized through log calculation of thefollowing Equation 7. In an embodiment, the product of Equation 7 may bealso referred to as the normalized cell diagnosis deviationD*_(diag,i)[k].D* _(diag,i) [k]=Log|SMA _(i) [k]−LMA _(i) [k]  <Equation 7>

FIG. 2 d shows a change in the normalized cell diagnosis deviationD*_(diag,i)[k] of each battery cell BC_(i) over time. The cell diagnosisdeviation D*_(diag,i)[k] is calculated using Equation 6. In FIG. 2 d ,the horizontal axis indicates time, and the vertical axis indicates thecell diagnosis deviation D*_(diag,i)[k] of each battery cell BC_(i).

Referring to FIG. 2 d , it can be seen that the change in short/longterm average difference of each battery cell BC_(i) is amplified on thebasis of the average value by the normalization of the short/long termaverage difference |SMA_(i)−LMA_(i)[k]| of each battery cell BC_(i).Accordingly, it is possible to achieve more accurate diagnosis ofabnormal voltage of the battery cell.

Preferably, the control circuit 220 may carry out abnormal voltagediagnosis of each battery cell BC_(i) by comparing the normalized celldiagnosis deviation D*_(diag,i)[k] of each battery cell BC_(i) with astatistical adaptive threshold D_(threshold)[k].

Preferably, the control circuit 220 may set the statistical adaptivethreshold D_(threshold)[k] using Equation 8 at each unit time.D _(threshold) [k]β*Sigma(D* _(diag,i) [k])  <Equation 8>

In Equation 8, Sigma is a function which calculates the standarddeviation for the normalized cell diagnosis deviation D*_(diag,i)[k] ofall the battery cells BC at the time index k. Additionally, β is anexperimentally determined constant. β is a factor which determinesdiagnosis sensitivity. When the present disclosure is performed on acell group including a battery cell in which abnormal voltage actuallyoccurred, β may be appropriately determined by trial and error to detectthe corresponding battery cell as an abnormal voltage cell. In anexample, β may be set to at least 5, or at least 6, or at least 7, or atleast 8, or at least 9. D_(threshold)[k] generated by Equation 8 ismultiple and constructs time series data.

Meanwhile, the normalized cell diagnosis deviation D*_(diag,i)[k] forthe battery cell in abnormal voltage condition is larger than that of anormal battery cell. Accordingly, to improve the accuracy andreliability of diagnosis, in the calculation of Sigma(D*_(diag,i)[k]) atthe time index k, it is desirable to exclude max(D*_(diag,i)[k])corresponding to the maximum value. Here, max is a function whichreturns a maximum value for a plurality of input parameters, and theinput parameters are the normalized cell diagnosis deviationsD*_(diag,i)[k] of all the battery cells.

In FIG. 2 d , the time series data indicating a change of thestatistical adaptive threshold D_(threshold)[k] over time corresponds tothe profile in the darkest color among all the profiles.

After determining the statistical adaptive threshold D_(threshold)[k] atthe time index k, the control circuit 220 may determine a filterdiagnosis value D_(filter,i)[k] by filtering the normalized celldiagnosis deviation D*_(diag,i)[k] of each battery cell BC_(i) using thefollowing Equation 9.

Two values may be assigned to the filter diagnosis value D_(filter,i)[k]for each battery cell BC_(i). That is, in case that the cell diagnosisdeviation D*_(diag,i)[k] is larger than the statistical adaptivethreshold D_(threshold)[k], a difference value between the celldiagnosis deviation D*_(diag,i)[k] and the statistical adaptivethreshold D_(Threshold)[k] is assigned to the filter diagnosis valueD_(filter,i)[k]. In contrast, in case that the cell diagnosis deviationD*_(diag,i)[k] is equal to or smaller than the statistical adaptivethreshold D_(threshold)[k], 0 is assigned to the filter diagnosis valueD_(filter,i)[k].D _(filter,i) [k]=D* _(diag,i) [k]−D _(threshold) [k](IF D* _(diag,i)[k]>D _(threshold) [k])D _(filter,i) [k]=0(IF D* _(diag,i) [k]≤D _(threshold) [k])  <Equation9>

FIG. 2 e is a diagram showing time series data of the filter diagnosisvalue D_(filter,i)[k] obtained through the filtering of the celldiagnosis deviation D*_(diag,i)[k] at the time index k.

Referring to FIG. 2 e , an irregular pattern shows that the filterdiagnosis value D_(filter,i)[k] of a specific battery cell has apositive value at about 3000 sec. For reference, the specific batterycell having the irregular pattern is the battery cell having the timeseries data indicated by A in FIG. 2 d.

In an example, the control circuit 220 may accumulate time step in whichthe filter diagnosis value D_(filter,i)[k] is larger than the diagnosisthreshold (for example, 0) in the time series data of the filterdiagnosis value D_(filter,i)[k] for each battery cell BC_(i), anddiagnose the battery cell which meets the requirement that theaccumulation time is larger than a preset reference time as the abnormalvoltage cell.

Preferably, the control circuit 220 may accumulate time step whichsuccessively meets the requirement that the filter diagnosis valueD_(filter,i)[k] is larger than the diagnosis threshold. When thecorresponding time step is multiple, the control circuit 220 mayindependently calculate the accumulation time at each time step.

In another example, the control circuit 220 may accumulate the number ofdata included in the time step in which the filter diagnosis valueD_(filter,i)[k] is larger than the diagnosis threshold (for example, 0)in the time series data of the filter diagnosis value D_(filter,i)[k]for each battery cell BC_(i), and diagnose the battery cell which meetsthe requirement that the data number accumulated value is larger than apreset reference count as the abnormal voltage cell.

Preferably, the control circuit 220 may accumulate only the number ofdata included in the time step which successively meets the requirementthat the filter diagnosis value D_(filter,i)[k] is larger than thediagnosis threshold. When the corresponding time step is multiple, thecontrol circuit 220 may independently accumulate the number of data ofeach time step.

Meanwhile, the control circuit 220 may replace V_(i)[k] of Equations 1to 5 with the normalized cell diagnosis deviation D*_(diag,i)[k] of eachbattery cell BC_(i) shown in FIG. 2 d . Additionally, the controlcircuit 220 may recursively perform the calculation of the short/longterm average difference |SMA_(i)[k]−LMA_(i)[k]| of the cell diagnosisdeviation D*_(diag,i)[k], calculation of the average value of short/longterm average differences |SMA_(i)[k]−LMA_(i)[k]| of the cell diagnosisdeviation D*_(diag,i)[k], calculation of the cell diagnosis deviationD*_(diag,i)[k] corresponding to a difference of the short/long termaverage difference |SMA_(i)[k]−LMA_(i)[k]| compared to the averagevalue, calculation of the normalized cell diagnosis deviationD*_(diag,i)[k] for the short/long term average difference|SMA_(i)[k]−LMA_(i)[k]| using Equation 6, determination of thestatistical adaptive threshold D_(threshold)[k] for the normalized celldiagnosis deviation D*_(diag,i)[k] using Equation 8, determination ofthe filter diagnosis value D_(filter,i)[k] through the filtering of thecell diagnosis deviation D*_(diag,i)[k] using Equation 9, and abnormalvoltage diagnosis of the battery cell using the time series data of thefilter diagnosis value D_(filter,i)[k], at the time index k.

FIG. 2 f is a graph showing a time change of the short/long term averagedifference |SMA_(i)[k]−LMA_(i)[k]| for the time series data (FIG. 2 d )of the normalized cell diagnosis deviation D*_(diag,i)[k]. In Equations2, 4 and 5 used to calculate the short/long term average difference|SMA_(i)[k]−LMA_(i)[k]|, V_(i)[k] may be replaced with D*_(diag,i)[k],and V_(av)[k] may be replaced with the average value of D*_(diag,i)[k].

FIG. 2 g is a graph showing the time series data of the normalized celldiagnosis deviation D*_(diag,i)[k] calculated using Equation 6. In FIG.2 g , the time series data of the statistical adaptive thresholdD_(threshold)[k] corresponds to the profile indicated in the darkestcolor.

FIG. 2 h is the profile showing the time series data of the filterdiagnosis value D_(filter,i)[k] obtained by filtering the time seriesdata of the cell diagnosis deviation D*_(diag,i)[k] using Equation 9.

In an example, the control circuit 220 may accumulate time step in whichthe filter diagnosis value (D_(filter,i)[k]) is larger than thediagnosis threshold (for example, 0) in the time series data of thefilter diagnosis value D_(filter,i)[k] for each battery cell BC_(i), anddiagnose the battery cell which meets the requirement that theaccumulation time is larger than the preset reference time as theabnormal voltage cell.

Preferably, the control circuit 220 may accumulate time step whichsuccessively meets the requirement that the filter diagnosis valueD_(filter,i)[k] is larger than the diagnosis threshold. When thecorresponding time step is multiple, the control circuit 220 mayindependently calculate the accumulation time at each time step.

In another example, the control circuit 220 may accumulate the number ofdata included in the time step in which the filter diagnosis valueD_(filter,i)[k] is larger than the diagnosis threshold (for example, 0)in the time series data of the filter diagnosis value D_(filter,i)[k]for each battery cell BC_(i), and diagnose the battery cell which meetsthe requirement that the data number accumulated value is larger thanthe preset reference count as the abnormal voltage cell.

Preferably, the control circuit 220 may accumulate only the number ofdata included in the time step which successively meets the requirementthat the filter diagnosis value D_(filter,i)[k] is larger than thediagnosis threshold. When the corresponding time step is multiple, thecontrol circuit 220 may independently accumulate the number of data ofeach time step.

The control circuit 220 may additionally repeat the above-describedrecursive calculation process a reference number of times. That is, thecontrol circuit 220 may replace the voltage time series data shown inFIG. 2 a with the time series data (for example, data of FIG. 2 g ) ofthe normalized cell diagnosis deviation D*_(diag,i)[k]. Additionally,the control circuit 220 may recursively perform the calculation of theshort/long term average difference |SMA_(i)[k]−LMA_(i)[k]|, calculationof the average value of the short/long term average difference|SMA_(i)[k]−LMA_(i)[k]|, calculation of the cell diagnosis deviationD_(diag,i)[k] corresponding to a difference of the short/long termaverage difference |SMA_(i)[k]−LMA_(i)[k]| compared to the averagevalue, calculation of the normalized cell diagnosis deviationD*_(diag,i)[k] for the short/long term average difference|SMA_(i)[k]−LMA_(i)[k]| using Equation 6, determination of thestatistical adaptive threshold D_(threshold)[k] for the cell diagnosisdeviation D*_(diag,i)[k] using Equation 8, determination of the filterdiagnosis value D_(filter,i)[k] through the filtering of the celldiagnosis deviation D*_(diag,i)[k] using Equation 9, and abnormalvoltage diagnosis of the battery cell using the time series data of thefilter diagnosis value D_(filter,i)[k], at the time index k.

When the recursive calculation process as described above is repeated,abnormal voltage diagnosis of the battery cell may be performed moreprecisely. That is, referring to FIG. 2 e , a positive profile patternis observed in only two time steps in the time series data of the filterdiagnosis value D_(filter,i)[k] of the battery cell in abnormal voltagecondition. However, referring to FIG. 2 h , a positive profile patternis observed over more time steps than FIG. 2 e in the time series dataof the filter diagnosis value D_(filter,i)[k] of the battery cell inabnormal voltage condition. Accordingly, when the recurrent computationprocess is iteratively performed, it is possible to detect the time atwhich the abnormal voltage of the battery cell occurred more accurately.

Hereinafter, a battery diagnosis method using the above-describedbattery diagnosis apparatus 200 of the present disclosure will bedescribed in detail. The operation of the control circuit 220 will bedescribed in more detail in various embodiments of the battery diagnosismethod.

FIG. 3 is a flowchart showing exemplarily a battery diagnosis methodaccording to a first embodiment of the present disclosure. The method ofFIG. 3 may be periodically performed by the control circuit 220 at eachunit time.

Referring to FIGS. 1 to 3 , in step S310, the control circuit 220collects the voltage signal indicating the cell voltage of each of theplurality of battery cells BC₁˜BC_(N) from the voltage sensing circuit210, and generates the time series data of cell voltage of each batterycell BC (see FIG. 2 a ). The number of time series data of cell voltageincreases by 1 at each unit time.

Preferably, V_(i)[k] or VD_(i)[k] of Equation 5 may be used as the cellvoltage.

In step S320, the control circuit 220 determines the first average cellvoltage SMA_(i)[k] (see Equations 1 and 2) and the second average cellvoltage LMA_(i)[k] (see Equations 3 and 4) of each battery cell BC_(i)based on the time series data of cell voltage of each battery cellBC_(i) (see FIG. 2 b ). The first average cell voltage SMA_(i)[k] is ashort term moving average of cell voltage of each battery cell BC_(i)over the first moving window having a first time length. The secondaverage cell voltage LMA_(i)[k] is a long term moving average of cellvoltage of each battery cell BC_(i) over the second moving window havinga second time length. V_(i)[k] or VD_(i)[k] may be used to calculate thefirst average cell voltage SMA_(i)[k] and the second average cellvoltage LMA_(i)[k].

In step S330, the control circuit 220 determines the short/long termaverage difference |SMA_(i)[k]−LMA_(i)[k]| of each battery cell BC_(i)(see FIG. 2 c ).

In step S340, the control circuit 220 determines the cell diagnosisdeviation D*_(diag,i)[k] of each battery cell BC_(i). The cell diagnosisdeviation D*_(diag,i)[k] is a deviation between the average value|SMA_(i)[k]−LMA_(i)[k]|_(av) of short/long term average differences ofall the battery cells and the short/long term average difference|SMA_(i)[k]−LMA_(i)[k]| of the i^(th) battery cell BC_(i).

In step S350, the control circuit 220 determines whether the diagnosistime has passed. The diagnosis time is preset. When the determination ofthe step S350 is YES, step S360 is performed, and when the determinationof the step S350 is NO, the steps S310 to S340 are repeated.

In step S360, the control circuit 220 generates the time series data ofthe cell diagnosis deviation D*_(diag,i)[k] of each battery cell BC_(i)collected for the diagnosis time.

In step S370, the control circuit 220 carries out abnormal voltagediagnosis of each battery cell BC_(i) by analyzing the time series datafor the cell diagnosis deviation D_(diag,i)[k].

In an example, the control circuit 220 may accumulate time step in whichthe cell diagnosis deviation D_(diag,i)[k] is larger than the diagnosisthreshold (for example, 0.015) in the time series data for the celldiagnosis deviation D_(diag,i)[k] of each battery cell BC_(i) anddiagnose the battery cell which meets the requirement that theaccumulation time is larger than the preset reference time as theabnormal voltage cell.

Preferably, the control circuit 220 may accumulate only time step whichsuccessively meets the requirement that the cell diagnosis deviationD_(diag,i)[k] is larger than the diagnosis threshold. When thecorresponding time step is multiple, the control circuit 220 mayindependently calculate the accumulation time at each time step.

In another example, the control circuit 220 may accumulate the number ofdata in which the cell diagnosis deviation D_(diag,i)[k] is larger thanthe diagnosis threshold (for example, 0.015) in the time series data forthe cell diagnosis deviation D_(diag,i)[k] of each battery cell BC_(i),and diagnose the battery cell which meets the requirement that the datanumber accumulated value is larger than the preset reference count asthe abnormal voltage cell.

Preferably, the control circuit 220 may accumulate only the number ofdata included in the time step which successively meets the requirementthat the cell diagnosis deviation D_(diag,i)[k] is larger than thediagnosis threshold. When the corresponding time step is multiple, thecontrol circuit 220 may independently accumulate the number of data ofeach time step.

FIG. 4 is a flowchart showing exemplarily a battery diagnosis methodaccording to a second embodiment of the present disclosure. The methodof FIG. 4 may be periodically performed by the control circuit 220 ateach unit time.

In the battery diagnose method of the second embodiment, the steps S310to S360 are, in substance, identical to the first embodiment, and itsdescription is omitted. After the step S360 is completed, step S380 isperformed.

In the step S380, the control circuit 220 generates the time series dataof the statistical adaptive threshold D_(threshold)[k] using Equation 8.The input of the Sigma function of Equation 8 is time series data forthe cell diagnosis deviation D_(diag,i)[k] of all the battery cellsgenerated in the step S360. Preferably, the maximum value of the celldiagnosis deviation D_(diag,i)[k] may be excluded from the input valueof the Sigma function. The cell diagnosis deviation D_(diag,i)[k] is adeviation for the short/long term average difference|SMA_(i)[k]−LMA_(i)[k]| relative to the average value.

In step S390, the control circuit 220 generates the time series data ofthe filter diagnosis value D_(filter,i)[k] by filtering the celldiagnosis deviation D_(diag,i)[k] of each battery cell BC_(i) usingEquation 9.

In using Equation 9, D*_(diag,i)[k] may be replaced with D_(diag,i)[k].

In step S400, the control circuit 220 carries out abnormal voltagediagnosis of each battery cell BC_(i) by analyzing the time series dataof the filter diagnosis value D_(filter,i)[k].

In an example, the control circuit 220 may accumulate time step in whichthe filter diagnosis value D_(filter,i)[k] is larger than the diagnosisthreshold (for example, 0) in the time series data of the filterdiagnosis value D_(filter,i)[k] for each battery cell BC_(i), anddiagnose the battery cell which meets the requirement that theaccumulation time is larger than the preset reference time as theabnormal voltage cell.

Preferably, the control circuit 220 may accumulate only time step whichsuccessively meets the requirement that the filter diagnosis valueD_(filter,i)[k] is larger than the diagnosis threshold. When thecorresponding time step is multiple, the control circuit 220 mayindependently calculate the accumulation time at each time step.

In another example, the control circuit 220 may accumulate the number ofdata included in the time step in which the filter diagnosis valueD_(filter,i)[k] is larger than the diagnosis threshold (for example, 0)in the time series data of the filter diagnosis value D_(filter,i)[k]for each battery cell BC_(i), and diagnose the battery cell which meetsthe requirement that the data number accumulation value is larger thanthe preset reference count as the abnormal voltage cell.

Preferably, the control circuit 220 may accumulate only the number ofdata included in the time step which successively meets the requirementthat the filter diagnosis value D_(filter,i)[k] is larger than thediagnosis threshold. When the corresponding time step is multiple, thecontrol circuit 220 may independently accumulate the number of data ofeach time step.

FIG. 5 is a flowchart showing exemplarily a battery diagnosis methodaccording to a third embodiment of the present disclosure. The method ofFIG. 5 may be periodically performed by the control circuit 220 at eachunit time.

The battery diagnosis method according to the third embodiment is, insubstance, identical to the first embodiment except that the steps S340,S360 and S370 are changed to steps S340′, 360′ and S370′. Accordingly,the third embodiment will be described with regard to the differences.

In the step S340′, the control circuit 220 determines the normalizedcell diagnosis deviation D*_(diag,i)[k] for the short/long term averagedifference |SMA_(i)[k]−LMA_(i)[k]| of each battery cell BC_(i) usingEquation 6. The normalization reference value is an average value of theshort/long term average difference |SMA_(i)[k]−LMA_(i)[k]|. Equation 6may be replaced with Equation 7.

In the step S360′, the control circuit 220 generates the time seriesdata for the normalized cell diagnosis deviation D*_(diag,i)[k] of eachbattery cell BC_(i) collected for the diagnosis time (see FIG. 2 d ).

In the step S370′, the control circuit 220 carries out abnormal voltagediagnosis of each battery cell BC_(i) by analyzing the time series datafor the normalized cell diagnosis deviation D*_(diag,i)[k].

In an example, the control circuit 220 may accumulate time step in whichthe cell diagnosis deviation D*_(diag,i)[k] is larger than the diagnosisthreshold (for example, 4) in the time series data for the normalizedcell diagnosis deviation D*_(diag,i)[k] of each battery cell BC_(i), anddiagnose the battery cell which meets the requirement that theaccumulation time is larger than the preset reference time as theabnormal voltage cell.

Preferably, the control circuit 220 may accumulate only time step whichsuccessively meets the requirement that the normalized cell diagnosisdeviation D*_(diag,i)[k] is larger than the diagnosis threshold. Whenthe corresponding time step is multiple, the control circuit 220 mayindependently calculate the accumulation time at each time step.

In another example, the control circuit 220 may accumulate the number ofdata in which the cell diagnosis deviation is larger than the diagnosisthreshold (for example, 4) in the time series data for the normalizedcell diagnosis deviation D*_(diag,i)[k] of each battery cell BC_(i), anddiagnose the battery cell which meets the requirement that the datanumber accumulated value is larger than the preset reference count asthe abnormal voltage cell.

Preferably, the control circuit 220 may accumulate only the number ofdata included in the time step which successively meets the requirementthat the normalized cell diagnosis deviation D*_(diag,i)[k] is largerthan the diagnosis threshold. When the corresponding time step ismultiple, the control circuit 220 may independently accumulate thenumber of data of each time step.

FIG. 6 is a flowchart showing exemplarily a battery diagnosis methodaccording to a fourth embodiment of the present disclosure. The methodof FIG. 6 may be periodically performed by the control circuit 220 ateach unit time.

The battery diagnosis method according to the fourth embodiment is, insubstance, identical to the second embodiment except that the stepsS340, S360, S380, S390 and S400 are changed to steps S340′, S360′,S380′, S390′ and S400′, respectively. Accordingly, the fourth embodimentwill be described with regard to the differences from the secondembodiment.

In the step S340′, the control circuit 220 determines the normalizedcell diagnosis deviation D*_(diag,i)[k] for the short/long term averagedifference |SMA_(i)[k]−LMA_(i)[k]| of each battery cell BC_(i) usingEquation 6. The normalization reference value is an average value of theshort/long term average difference |SMA_(i)[k]−LMA_(i)[k]|. Equation 6may be replaced with Equation 7.

In the step S360′, the control circuit 220 generates the time seriesdata for the normalized cell diagnosis deviation D*_(diag,i)[k] of eachbattery cell BC_(i) collected for the diagnosis time (see FIG. 2 d ).

In the step S380′, the control circuit 220 generates the time seriesdata of the statistical adaptive threshold D_(threshold)[k] usingEquation 8. The input of the Sigma function of Equation 8 is time seriesdata for the normalized cell diagnosis deviation D*_(diag,i)[k] of allthe battery cells generated in the step S360′. Preferably, at each timeindex, the maximum value of the cell diagnosis deviation D*_(diag,i)[k]may be excluded from the input value of the Sigma function.

In the step S390′, the control circuit 220 generates the time seriesdata of the filter diagnosis value D_(filter,i)[k] by filtering the celldiagnosis deviation D*_(diag,i)[k] of each battery cell BC_(i) on thebasis of the statistical adaptive threshold D_(threshold)[k] usingEquation 9.

In the step S400′, the control circuit 220 carries out abnormal voltagediagnosis of each battery cell BC_(i) by analyzing the time series dataof the filter diagnosis value D_(filter,i)[k].

In an example, the control circuit 220 may accumulate time step in whichthe filter diagnosis value D_(filter,i)[k] is larger than the diagnosisthreshold (for example, 0) in the time series data of the filterdiagnosis value D_(filter,i)[k] for each battery cell BC_(i), anddiagnose the battery cell which meets the requirement that theaccumulation time is larger than the preset reference time as theabnormal voltage cell.

Preferably, the control circuit 220 may accumulate time step whichsuccessively meets the requirement that the filter diagnosis valueD_(filter,i)[k] is larger than the diagnosis threshold. When thecorresponding time step is multiple, the control circuit 220 mayindependently calculate the accumulation time at each time step.

In another example, the control circuit 220 may accumulate the number ofdata included in the time step in which the filter diagnosis valueD_(filter,i)[k] is larger than the diagnosis threshold (for example, 0)in the time series data of the filter diagnosis value D_(filter,i)[k]for each battery cell BC_(i), and diagnose the battery cell which meetsthe requirement that the data number accumulated value is larger thanthe preset reference count as the abnormal voltage cell.

Preferably, the control circuit 220 may accumulate only the number ofdata included in the time step which successively meets the requirementthat the filter diagnosis value D_(filter,i)[k] is larger than thediagnosis threshold. When the corresponding time step is multiple, thecontrol circuit 220 may independently accumulate the number of data ofeach time step.

FIG. 7 is a flowchart showing exemplarily a battery diagnosis methodaccording to a fifth embodiment of the present disclosure.

In the fifth embodiment, the steps S310 to S360′ are, in substance,identical to the fourth embodiment. Accordingly, the fifth embodimentwill be described with regard to differences compared to the fourthembodiment.

In step S410, the control circuit 220 generates the first moving averageSMA_(i)[k] time series data and the second moving average LMA_(i)[k]time series data for the cell diagnosis deviation D*_(diag,i)[k] usingthe normalized cell diagnosis deviation D*_(diag,i)[k] time series dataof each battery cell BC_(i) (see FIG. 2 f ).

In step S420, the control circuit 220 generates the normalized celldiagnosis deviation D*_(diag,i)[k] time series data using the firstmoving average SMA_(i)[k] time series data and the second moving averageLMA_(i)[k] time series data of each battery cell BC_(i) using Equation 6(see FIG. 2 g ).

In step S430, the control circuit 220 generates the time series data ofthe statistical adaptive threshold D_(threshold)[k] using Equation 8(see FIG. 2 g ).

In step S440, the control circuit 220 generates the time series data forthe filter diagnosis value D_(filter,i)[k] of each battery cell BC_(i)on the basis of the statistical adaptive threshold D_(threshold)[k]using Equation 9 (see FIG. 2 h ).

In step S450, the control circuit 220 carries out abnormal voltagediagnosis of each battery cell BC_(i) by analyzing the time series dataof the filter diagnosis value D_(filter,i)[k] of each battery cellBC_(i).

In an example, the control circuit 220 may accumulate time step in whichthe filter diagnosis value D_(filter,i)[k] is larger than the diagnosisthreshold (for example, 0) in the time series data of the filterdiagnosis value D_(filter,i)[k] for each battery cell BC_(i), anddiagnose the battery cell which meets the requirement that theaccumulation time is larger than the preset reference time as theabnormal voltage cell.

Preferably, the control circuit 220 may accumulate time step whichsuccessively meets the requirement that the filter diagnosis valueD_(filter,i)[k] is larger than the diagnosis threshold. When thecorresponding time step is multiple, the control circuit 220 mayindependently calculate the accumulation time at each time step.

In another example, the control circuit 220 may accumulate the number ofdata included in the time step in which the filter diagnosis valueD_(filter,i)[k] is larger than the diagnosis threshold (for example, 0)in the time series data of the filter diagnosis value D_(filter,i)[k]for each battery cell BC_(i), and diagnose the battery cell which meetsthe requirement that the data number accumulated value is larger thanthe preset reference count as the abnormal voltage cell.

Preferably, the control circuit 220 may accumulate only the number ofdata included in the time step which successively meets the requirementthat the filter diagnosis value D_(filter,i)[k] is larger than thediagnosis threshold. When the corresponding time step is multiple, thecontrol circuit 220 may independently accumulate the number of data ofeach time step.

In the fifth embodiment, the control circuit 220 may recursively performthe steps S410 and S420 at least twice. That is, the control circuit 220may generate the first moving average SMA_(i)[k] time series data andthe second moving average LMA_(i)[k] time series data for the celldiagnosis deviation D*_(diag,i)[k] again in the step S410 using thenormalized cell diagnosis deviation D*_(diag,i)[k] time series datagenerated in the step S420. Subsequently, the control circuit 220 maygenerate the normalized cell diagnosis deviation D*_(diag,i)[k] timeseries data based on Equation 6 using the first moving averageSMA_(i)[k] time series data and the second moving average LMA_(i)[k]time series data of each battery cell BC_(i) again in the step S420. Therecursive algorithm may be repeated a preset number of times.

When the steps S410 and S420 are performed according to the recursivealgorithm, the steps S430 to S450 may be performed using the celldiagnosis deviation D*_(diag,i)[k] time series data finally calculatedthrough the recursive algorithm.

In an embodiment of the present disclosure, the control circuit 220 mayoutput diagnosis result information through a display unit (not shown)when abnormal voltage in specific battery cell(s) is diagnosed afterabnormal voltage diagnosis for all the battery cells. Additionally, thecontrol circuit 220 may record identification information (ID) of thebattery cell in which the abnormal voltage was diagnosed, the time whenthe abnormal voltage was diagnosed and a diagnosis flag in the memoryunit.

Preferably, the diagnosis result information may include a messageindicating the presence of a cell in abnormal voltage condition in thecell group. Optionally, the diagnosis result information may include awarning message that it is necessary to precisely inspect the batterycells.

In an example, the display unit may be included in a load device that issupplied with power from the cell group CG. When the load device is anelectric vehicle, a hybrid electric vehicle or a plug-in hybrid electricvehicle, the diagnosis result information may be output through acluster information display. In another example, when the batterydiagnosis apparatus 200 according to the present disclosure is includedin a diagnosis system, the diagnosis result may be output through adisplay provided in the diagnosis system.

Preferably, the battery diagnosis apparatus 200 according to anembodiment of the present disclosure may be included in the batterymanagement system 100 or a control system (not shown) of the loaddevice.

According to the above-described embodiments, it is possible to achieveefficient and accurate diagnosis of abnormal voltage of each batterycell by determining two moving averages of cell voltage of each batterycell over two different time lengths at each unit time, and carrying outabnormal voltage diagnosis of each battery cell based on a differencebetween the two moving averages of each of the plurality of batterycells.

According to another aspect, it is possible to achieve accuratediagnosis of abnormal voltage of each battery cell by applying theadvanced techniques such as normalization and/or the statisticaladaptive threshold to analyze a difference in variation trend of twomoving averages of each battery cell.

According to still another aspect, it is possible to precisely detectthe time step in which the abnormal voltage of each battery celloccurred and/or the abnormal voltage detection count by analyzing thetime series data of the filter diagnosis value determined based on thestatistical adaptive threshold.

The embodiments of the present disclosure described hereinabove are notimplemented only through the apparatus and method, and may beimplemented through programs that perform functions corresponding to theconfigurations of the embodiments of the present disclosure or recordingmedia having the programs recorded thereon, and such implementation maybe easily achieved by those skilled in the art from the disclosure ofthe embodiments described above.

While the present disclosure has been hereinabove described with regardto a limited number of embodiments and drawings, the present disclosureis not limited thereto and it is obvious to those skilled in the artthat various modifications and changes may be made thereto within thetechnical aspects of the present disclosure and the equivalent scope ofthe appended claims.

Additionally, as many substitutions, modifications and changes may bemade to the present disclosure described hereinabove by those skilled inthe art without departing from the technical aspects of the presentdisclosure, the present disclosure is not limited by the above-describedembodiments and the accompanying drawings, and some or all of theembodiments may be selectively combined to allow various modifications.

What is claimed is:
 1. A battery diagnosis apparatus for a cell groupincluding a plurality of battery cells connected in series, the batterydiagnosis apparatus comprising: a voltage sensing circuit configured toperiodically generate a voltage signal indicating a cell voltage of eachbattery cell of the plurality of battery cells; and a control circuitconfigured to: for each battery cell of the plurality of battery cells,generate time series data indicating a change in cell voltage of thebattery cell over time based on the voltage signal; for each batterycell of the plurality of battery cells, determine a first average cellvoltage and a second average cell voltage of the battery cell based onthe time series data, wherein the first average cell voltage is a shortterm moving average, and the second average cell voltage is a long termmoving average; and detect an abnormal voltage of at least one batterycell based on a difference between the first average cell voltage andthe second average cell voltage of the at least one battery cell.
 2. Thebattery diagnosis apparatus according to claim 1, wherein the controlcircuit is configured to: for each battery cell of the plurality ofbattery cells, determine a short/long term average difference of thebattery cell corresponding to the difference between the first averagecell voltage and the second average cell voltage of the battery cell;for each battery cell of the plurality of battery cells, determine acell diagnosis deviation of the battery cell corresponding to adeviation between an average value of short/long term averagedifferences of all the plurality of battery cells and the short/longterm average difference of the battery cell; and in response to the celldiagnosis deviation exceeding a diagnosis threshold for the at least onebattery cell, determine that the at least one battery cell is anabnormal voltage cell.
 3. The battery diagnosis apparatus according toclaim 2, wherein the control circuit is configured to, for each batterycell of the plurality of battery cells, generate time series data of thecell diagnosis deviation of the battery cell, and determine that atleast one battery cell of the plurality of battery cells is an abnormalvoltage cell either (i) after a period of time during which the celldiagnosis deviation exceeds the diagnosis threshold or (ii) after anumber of cell diagnosis deviations exceeding the diagnosis thresholdexceeds a predetermined number.
 4. The battery diagnosis apparatusaccording to claim 1, wherein the control circuit is configured to: foreach battery cell of the plurality of battery cells, determine ashort/long term average difference of the battery cell corresponding tothe difference between the first average cell voltage and the secondaverage cell voltage of the battery cell; for each battery cell of theplurality of battery cells, determine a cell diagnosis deviation of thebattery cell corresponding to a deviation between an average value ofshort/long term average differences of all the plurality of batterycells and the short/long term average difference of the battery cell;determine a statistical adaptive threshold based on a standard deviationfor the cell diagnosis deviation of all the plurality of battery cells;for each battery cell of the plurality of battery cells, generate timeseries data of a filter diagnosis value of the battery cell by filteringtime series data for the cell diagnosis deviation of the battery cellbased on the statistical adaptive threshold; and detect the abnormalvoltage of the at least one battery cell based on a period of timeduring which the filter diagnosis value of the at least one battery cellexceeds a diagnosis threshold, or based on a number of the filterdiagnosis value exceeding the diagnosis threshold.
 5. The batterydiagnosis apparatus according to claim 1, wherein the control circuit isconfigured to: for each battery cell of the plurality of battery cells,determine a short/long term average difference of the battery cellcorresponding to the difference between the first average cell voltageand the second average cell voltage of the battery cell, for eachbattery cell of the plurality of battery cells, determine anormalization value of the short/long term average difference of thebattery cell as a normalized cell diagnosis deviation, determine astatistical adaptive threshold based on a standard deviation for thenormalized cell diagnosis deviation of all the plurality of batterycells, for each battery cell of the plurality of battery cells, generatetime series data of a filter diagnosis value by filtering time seriesdata for the normalized cell diagnosis deviation of the battery cellbased on the statistical adaptive threshold, and detect the abnormalvoltage of at least one battery cell based on a period of time duringwhich the filter diagnosis value of the at least one battery cellexceeds a diagnosis threshold, or based on a number of the filterdiagnosis value exceeding the diagnosis threshold.
 6. The batterydiagnosis apparatus according to claim 5, wherein the control circuit isconfigured to, for each battery cell of the plurality of battery cells,normalize the short/long term average difference of the battery cell bydividing the short/long term average difference of the battery cell byan average value of short/long term average differences of all theplurality of battery cells.
 7. The battery diagnosis apparatus accordingto claim 5, wherein the control circuit is configured to, for eachbattery cell of the plurality of battery cells, normalize the short/longterm average difference of the battery cell through log calculation ofthe short/long term average difference of the battery cell.
 8. Thebattery diagnosis apparatus according to claim 1, wherein the controlcircuit is configured to, for each battery cell of the plurality ofbattery cells, generate time series data indicating a change in cellvoltage of the battery cell over time using a voltage difference betweena cell voltage average value of all the plurality of battery cells and acell voltage of the battery cell, measured at each unit time.
 9. Thebattery diagnosis apparatus according to claim 1, wherein the controlcircuit is configured to: for each battery cell of the plurality ofbattery cells, determine a short/long term average difference of thebattery cell corresponding to the difference between the first averagecell voltage and the second average cell voltage of the battery cell;for each battery cell of the plurality of battery cells, determine anormalization value of the short/long term average difference of thebattery cell as a normalized cell diagnosis deviation of the batterycell, and generate time series data of the normalized cell diagnosisdeviation of the battery cell; for each battery cell of the plurality ofbattery cells, generate the time series data of the normalized celldiagnosis deviation of the battery cell by recursively repeating: (i)determining a first moving average and a second moving average of thebattery cell for the time series data of the normalized cell diagnosisdeviation of the battery cell, wherein the first moving average is ashort term moving average of the battery cell and the second movingaverage is a long term moving average of the battery cell, (ii)determining the short/long term average difference of the battery cellcorresponding to a difference between the first moving average and thesecond moving average of the battery cell, (iii) determining thenormalization value of the short/long term average difference of thebattery cell as the normalized cell diagnosis deviation, and (iv)generating the time series data of the normalized cell diagnosisdeviation of the battery cell; determine a statistical adaptivethreshold based on a standard deviation for the normalized celldiagnosis deviation of all the plurality of battery cells; for eachbattery cell of the plurality of battery cells, generate time seriesdata of a filter diagnosis value of the battery cell by filtering thetime series data for the normalized cell diagnosis deviation of thebattery cell based on the statistical adaptive threshold; and detect theabnormal voltage of at least one battery cell based on a period of timeduring which the filter diagnosis value of the at least one battery cellexceeds a diagnosis threshold, or based on a number of the filterdiagnosis value exceeding the diagnosis threshold.
 10. A battery packcomprising the battery diagnosis apparatus according to claim
 1. 11. Avehicle comprising the battery pack according to claim
 10. 12. A batterydiagnosis method for a cell group including a plurality of battery cellsconnected in series, the battery diagnosis method comprising: (a) foreach battery cell of the plurality of battery cells, periodicallygenerating, by one or more processors, time series data indicating achange in cell voltage of the battery cell over time; (b) for eachbattery cell of the plurality of battery cells, determining, by the oneor more processors, a first average cell voltage and a second averagecell voltage of the battery cell based on the time series data, whereinthe first average cell voltage is a short term moving average, and thesecond average cell voltage is a long term moving average; and (c)detecting, by the one or more processors, an abnormal voltage of atleast one battery cell based on a difference between the first averagecell voltage and the second average cell voltage of the at least onebattery cell.
 13. The battery diagnosis method according to claim 12,wherein the step (c) comprises: (c1) for each battery cell of theplurality of battery cells, determining, by the one or more processors,a short/long term average difference of the battery cell correspondingto the difference between the first average cell voltage and the secondaverage cell voltage of the battery cell; (c2) determining, by the oneor more processors, a cell diagnosis deviation of the battery cellcorresponding to a deviation between an average value of short/long termaverage differences of all the plurality of battery cells and theshort/long term average difference of the battery cell of the batterycell; and (c3) in response to the cell diagnosis deviation exceeding adiagnosis threshold for the at least one battery cell, determining, bythe one or more processors, that the at least one battery cell is anabnormal voltage cell.
 14. The battery diagnosis method according toclaim 13, wherein the step (c) comprises: (c1) for each battery cell ofthe plurality of battery cells, generating, by the one or moreprocessors, time series data of the cell diagnosis deviation of thebattery cell, wherein determining that at least one battery cell of theplurality of battery cells is an abnormal voltage cell occurs eitherafter a period of time during which the cell diagnosis deviation exceedsthe diagnosis threshold or after a number of cell diagnosis deviationsexceeding the diagnosis threshold exceeds a predetermined number. 15.The battery diagnosis method according to claim 12, wherein the step (c)comprises: (c1) for each battery cell of the plurality of battery cells,determining, by the one or more processors, a short/long term averagedifference of the battery cell corresponding to the difference betweenthe first average cell voltage and the second average cell voltage ofthe battery cell; (c2) determining, by the one or more processors, acell diagnosis deviation of the battery cell corresponding to adeviation between an average value of short/long term averagedifferences of all the plurality of battery cells and the short/longterm average difference of the battery cell; (c3) determining, by theone or more processors, a statistical adaptive threshold based on astandard deviation for the cell diagnosis deviation of all the pluralityof battery cells; (c4) for each battery cell of the plurality of batterycells, generating, by the one or more processors, time series data of afilter diagnosis value of the battery cell by filtering time series datafor the cell diagnosis deviation of the battery cell based on thestatistical adaptive threshold; and (c5) detecting, by the one or moreprocessors, the abnormal voltage of the at least one battery cell basedon a period of time during which the filter diagnosis value of the atleast one battery cell exceeds a diagnosis threshold, or based on anumber of the filter diagnosis value exceeding the diagnosis threshold.16. The battery diagnosis method according to claim 12, wherein the step(c) comprises: (c1) for each battery cell of the plurality of batterycells, determining, by the one or more processors, a short/long termaverage difference of the battery cell corresponding to the differencebetween the first average cell voltage and the second average cellvoltage of the battery cell; (c2) for each battery cell of the pluralityof battery cells, determining, by the one or more processors, anormalization value of the short/long term average difference of thebattery cell as a normalized cell diagnosis deviation; (c3) determining,by the one or more processors, a statistical adaptive threshold based ona standard deviation for the normalized cell diagnosis deviation of allthe plurality of battery cells; (c4) for each battery cell of theplurality of battery cells, generating, by the one or more processors,time series data of a filter diagnosis value by filtering time seriesdata for the normalized cell diagnosis deviation of the battery cellbased on the statistical adaptive threshold; and (c5) detecting, by theone or more processors, the abnormal voltage of the at least one batterycell based on a period of time during which the filter diagnosis valueof the at least one battery cell exceeds a diagnosis threshold, or basedon a number of data of the filter diagnosis value exceeding thediagnosis threshold.
 17. The battery diagnosis method according to claim16, wherein the step (c2) comprises for each battery cell of theplurality of battery cells, normalizing, by the one or more processors,the short/long term average difference of the battery cell by dividingthe short/long term average difference of the battery cell by an averagevalue of short/long term average differences of all the plurality ofbattery cells.
 18. The battery diagnosis method according to claim 16,wherein the step (c2) comprises, for each battery cell of the pluralityof battery cells, normalizing, by the one or more processors, theshort/long term average difference of the battery cell through logcalculation of the short/long term average difference of the batterycell.
 19. The battery diagnosis method according to claim 12, whereinthe step (a) comprises, for each battery cell of the plurality ofbattery cells, generating, by the one or more processors, time seriesdata indicating a change in cell voltage of the battery cell over timeusing a voltage difference between a cell voltage average value of allthe plurality of battery cells and a cell voltage of the battery cell,measured at each unit time.
 20. The battery diagnosis method accordingto claim 12, wherein the step (c) comprises: (c1) for each battery cellof the plurality of battery cells, determining, by the one or moreprocessors, a short/long term average difference of the battery cellcorresponding to the difference between the first average cell voltageand the second average cell voltage of the battery cell; (c2) for eachbattery cell of the plurality of battery cells, determining, by the oneor more processors, a normalization value of the short/long term averagedifference of the battery as a normalized cell diagnosis deviation ofthe battery cell; (c3) for each battery cell of the plurality of batterycells, generating, by the one or more processors, time series data ofthe normalized cell diagnosis deviation of the battery cell; (c4) foreach battery cell of the plurality of battery cells, generating, by theone or more processors, the time series data of the normalized celldiagnosis deviation for each battery cell by recursively repeating: (i)determining, by the one or more processors, a first moving average and asecond moving average of the battery cell for the time series data ofthe normalized cell diagnosis deviation of the battery cell, wherein thefirst moving average is a short term moving average of the battery celland the second moving average is a long term moving average of thebattery cell, (ii) determining the short/long term average difference ofthe battery cell corresponding to a difference between the first movingaverage and the second moving average of the battery cell, (iii)determining the normalization value of the short/long term averagedifference of the battery cell as the normalized cell diagnosisdeviation, and (iv) generating the time series data of the normalizedcell diagnosis deviation of the battery cell, (c5) determining, by theone or more processors, a statistical adaptive threshold based on astandard deviation for the normalized cell diagnosis deviation of allthe plurality battery cells; (c6) for each battery cell of the pluralityof battery cells, generating, by the one or more processors, time seriesdata of a filter diagnosis value of the battery cell by filtering thetime series data for the normalized cell diagnosis deviation of thebattery cell based on the statistical adaptive threshold; and (c7)detecting, by the one or more processors, the abnormal voltage of atleast one battery cell based on a period of time during which the filterdiagnosis value of the at least one battery cell exceeds a diagnosisthreshold, or based on a number of the filter diagnosis value exceedingthe diagnosis threshold.